OpenCV3.4.2-YOLOv3实现视频对象检测

下载预训练模型放入工程目录，放入run.mp4视频
yolov3.cfg
yolov3.weights
coco.names

#include <opencv2/opencv.hpp>
#include <opencv2/dnn/dnn.hpp>
#include <vector>using namespace std;
using namespace cv;
using namespace cv::dnn;// Initialize the parameters
float confThreshold = 0.2; // Confidence threshold
float nmsThreshold = 0.4;// Non-maximum suppression threshold
int inpWidth = 416;// Width of network's input image
int inpHeight = 416;// Height of network's input image
vector<string> classes;// Draw the predicted bounding box
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
{//Draw a rectangle displaying the bounding boxrectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255));//Get the label for the class name and its confidencestring label = format("%.2f", conf);if (!classes.empty()){CV_Assert(classId < (int)classes.size());label = classes[classId] + ":" + label;}//Display the label at the top of the bounding boxint baseLine;Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);top = max(top, labelSize.height);putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255, 255, 255));}// Get the names of the output layers
vector<String> getOutputsNames(const Net& net)
{static vector<String> names;if (names.empty()){//Get the indices of the output layers, i.e. the layers with unconnected outputsvector<int> outLayers = net.getUnconnectedOutLayers();//get the names of all the layers in the networkvector<String> layersNames = net.getLayerNames();// Get the names of the output layers in namesnames.resize(outLayers.size());for (size_t i = 0; i < outLayers.size(); ++i)names[i] = layersNames[outLayers[i] - 1];}return names;
}// Remove the bounding boxes with low confidence using non-maxima suppression
void postprocess(Mat& frame, const vector<Mat>& outs)
{vector<int> classIds;vector<float> confidences;vector<Rect> boxes;for (size_t i = 0; i < outs.size(); ++i){// Scan through all the bounding boxes output from the network and keep only the// ones with high confidence scores. Assign the box's class label as the class// with the highest score for the box.float* data = (float*)outs[i].data;for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols){Mat scores = outs[i].row(j).colRange(5, outs[i].cols);Point classIdPoint;double confidence;// Get the value and location of the maximum scoreminMaxLoc(scores, 0, &confidence, 0, &classIdPoint);if (confidence > confThreshold){int centerX = (int)(data[0] * frame.cols);int centerY = (int)(data[1] * frame.rows);int width = (int)(data[2] * frame.cols);int height = (int)(data[3] * frame.rows);int left = centerX - width / 2;int top = centerY - height / 2;classIds.push_back(classIdPoint.x);confidences.push_back((float)confidence);boxes.push_back(Rect(left, top, width, height));}}}// Perform non maximum suppression to eliminate redundant overlapping boxes with// lower confidencesvector<int> indices;NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);for (size_t i = 0; i < indices.size(); ++i){int idx = indices[i];Rect box = boxes[idx];drawPred(classIds[idx], confidences[idx], box.x, box.y, box.x + box.width, box.y + box.height, frame);}
}void main()
{// Load names of classesstring classesFile = "coco.names";ifstream ifs(classesFile.c_str());string line;while (getline(ifs, line)) classes.push_back(line);// Give the configuration and weight files for the modelString modelConfiguration = "yolov3.cfg";String modelWeights = "yolov3.weights";// Load the networkNet net = readNetFromDarknet(modelConfiguration, modelWeights);net.setPreferableBackend(DNN_BACKEND_OPENCV);net.setPreferableTarget(DNN_TARGET_CPU);cv::VideoCapture cap("run.mp4");cv::Mat frame, blob;while (waitKey(1) < 0){// get frame from the videocap >> frame;//std::cout << "frame.size=" << frame.size() << "\n";// Create a 4D blob from a frame.cv::Mat sml;cv::resize(frame, sml, cv::Size(0, 0), 3.0 / 2, 3 / 2.0);blobFromImage(sml, blob, 1 / 255.0, cv::Size(inpWidth, inpHeight), Scalar(0, 0, 0), true, false);//Sets the input to the networknet.setInput(blob);// Runs the forward pass to get output of the output layersvector<Mat> outs;net.forward(outs, getOutputsNames(net));// Remove the bounding boxes with low confidencepostprocess(frame, outs);// Put efficiency information. The function getPerfProfile returns the// overall time for inference(t) and the timings for each of the layers(in layersTimes)vector<double> layersTimes;double freq = getTickFrequency() / 1000;double t = net.getPerfProfile(layersTimes) / freq;string label = format("Inference time for a frame : %.2f ms", t);putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255));// Write the frame with the detection boxes  Mat detectedFrame;frame.convertTo(detectedFrame, CV_8U);cv::imshow("detectedFrame", detectedFrame);//cv::waitKey(10);}}

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